Last Updated On:  2022-09-19 19:23:12.063245
Author: Philipe Bujold

TOC:

• Demographics
• Social Media
• Food Waste
• Green Energy vs Community Solar
• EV Rental vs EV Purchasing Media
• Plant-Based Diets

Project Overview

CC iDTC x BE.Center Survey Sprint

---

Two areas of focus: End consumers and key influencers (not just in social media terms)

Goal

One quick survey to rule them all. The topics covered will be:

Exploratory:

• EV rental behavior/beliefs
• Climate and social media reputation
• Food - Waste
• Food - Plant-based Diets

Comparisons:

• EV rental metrics vs EV purchasing metrics
• Community solar metrics vs Green energy metrics

c:\Anaconda\envs\pandas-profiling\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning:

Columns (5,168,233) have mixed types.Specify dtype option on import or set low_memory=False.

Once imported, the data it filtered to keep only those records that were considered complete, and for participants aged over 18.

Original dataframe lenght: 4180
...
Lenght after filtering: 4180

Clean/Rename meta data

Columns that contain string responses (i.e., non-categorical data that must be transformed)

Index(['Vrid', 'Vstatus', 'Vcid', 'Vcomment', 'Vlanguage', 'Vreferer',
       'Vsessionid', 'Vuseragent', 'Vip', 'Vlong', 'Vlat', 'VGeoCountry',
       'VGeoCity', 'VGeoRegion', 'Vpostal', 'RID', 'var428', 'var200O1406Othr',
       'var201O1415Othr', 'var212O1440Othr', 'var218', 'var237', 'var238',
       'var261', 'var427O2785Othr', 'var269', 'var289O1890Othr', 'var290',
       'var362O2240Othr', 'var364O2246Othr', 'var365O2257Othr',
       'var452O2784Othr', 'var376O2327Othr', 'var441O2711Othr', 'zip',
       'gender_self_describe', 'hispanic_other', 'var306O1943Othr',
       'politics'],
      dtype='object')

Alchemer did a few mishaps when ordering data, manually reordering some of the yes/no answers.

reordered question:'Are you actively trying to reduce your food waste?'
reordered question:'Does your household pay for their own electricity?'
reordered question:'Before taking this survey, had you considered using community solar in the last 12 months?'
reordered question:'If people have the choice, should they choose community solar because it is the right thing to do?'
reordered question:'Does your household pay for their own electricity?'
reordered question:'Before taking this survey, had you considered using green energy in the last 12 months?'
reordered question:'If people have the choice, should they choose a green energy provider because it is the right thing to do?'
reordered question:'You noted that you are trying to eat less or no meat  Is this something you’ve shared with others?'
reordered question:'Are there any people or groups of people in your life you think feel strongly against eating less (or no) meat?'
reordered question:'Before taking this survey, have you ever considered renting a fully electric car? Car rentals can be daily or hourly rentals'
reordered question:'Do you think that people who rent cars should rent fully electric cars because it is the right thing to do?'
reordered question:'Before taking this survey, have you ever considered getting a fully electric car?'
reordered question:'Do you think that people should drive a fully electric car because it is the right thing to do?'

---

Demographics

The sample is slightly skewed from general US census data (as well as SASSY data) - reweighing will correct this.

All data averages are reweighed on ethnicity and SASSY scores.

---

Social Media

To understand if the general population (not social media influencers) believes there is a reputational risk associated with posting on social media about climate change, and how that compares to posting about activities that are environmentally positive but not explicitly climate-motivated.

What we want to be able to say

• Would someone bulk at posting something?
• What attributes do they have

This first set of questions examined which social media respondents used, which ones they used regularly, and which ones they actually created content for. The scores presented in the table represent the weighted average of % respondents who use or post on X social media platform.

	Facebook	Instagram	Pinterest	Reddit	Snapchat	TikTok	Twitter	YouTube	Other - Please specify	I don't use any social media
What social media platforms do you use? Select all that apply	80.46	51.13	32.46	16.96	28.08	35.60	31.47	72.10	3.93	4.02
What social media platforms do you use daily? Select all that apply	70.12	35.64	12.88	8.12	18.55	26.59	18.68	51.87	3.64	5.65
What social media platforms do you post or share content to? Select all that apply	71.86	35.54	9.03	6.09	16.75	15.48	17.54	20.17	3.98	8.28

We can see here some of the other social media that respondents used, as well as the demographic information of said respondents.

	education	income	age_group	SASSY	politics	state	sex	Other SMs used	Other SM used daily	Other SM posts on
Loading... (need help?)
None	education	income	age_group	SASSY	politics	state	sex	Other SMs used	Other SM used daily	Other SM posts on

This second set of questions examined which topics respondents were more likely to be interested in, post about, and face social praise or backlash for engaging with. Resondents were given a 7-item likert scale for which to answer from Extremely likely to Extremely unlikely, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Extremely likely, whilst 0 represents an average response of Extremely unlikely.

	Health and Fitness	Personal finance (money saving strategies, life hacks, etc )	Extreme weather (floods, heatwaves, storms, etc )	Technology and gadgets	Climate change	Food (new restaurants, recipes, ingredients, etc )	Lifestyles (Travel, hobbies, work )
Thinking about the social media you use, how interested are you when you see social media content about	0.48	0.46	0.53	0.48	0.42	0.56	0.49
Thinking about your typical posting habits, how likely are you to use social media in the future to post or share content with others about	0.48	0.41	0.49	0.47	0.41	0.58	0.55

c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

	Health and Fitness	Personal finance (money saving strategies, life hacks, etc )	Extreme weather (floods, heatwaves, storms, etc )	Technology and gadgets	Climate change	Food (new restaurants, recipes, ingredients, etc )	Lifestyles (Travel, hobbies, work )
How likely is it that you will receive a positive reaction (content sharing, likes, positive comments, direct messages, etc ) if you posted on social media about	0.56	0.50	0.54	0.55	0.47	0.65	0.60
How likely is it that you will receive a negative reaction (content sharing, dislikes, negative comments, direct messages, etc ) if you posted on social media about	0.38	0.39	0.40	0.38	0.44	0.36	0.37

---

Waste

To understand what food waste avoidance strategies people are already employing.

What we want to be able to say

• that people are moving towards wasting less, and that there is desire for programs that support that move.

Below you can see how many respondents are actively trying to reduce food wastem as well as a table showing the steps that those who are trying to reduce waste are taking.

	education	income	age_group	SASSY	politics	state	sex	Can you tell us a bit more about the steps you are taking to reduce food waste?
Loading... (need help?)
None	education	income	age_group	SASSY	politics	state	sex	Can you tell us a bit more about the steps you are taking to reduce food waste?

The next table show the weighted average engagement of respondents with specific behaviors. Resondents were given a 7-item likert scale for which to answer from Every time to Never, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Every time, whilst 0 represents an average response of Never.

	We all do different things when it comes to shopping for food and cooking How often do you do the following behaviors?
I make a grocery list when I go grocery shopping	0.699223
I stick to use-by/best-before labels	0.664936
I adjust plans for what I am cooking based on what I have in the fridge	0.672528
I compost food scraps / other food waste instead of putting it in the garbage	0.296812
I check my fridge and pantry before buying food	0.752424
I pack my fridge to the brim	0.394508
I make a point to use up food that is about to expire	0.717997

The next table show the weighted average response of participants beliefs around why reducing food waste is important.

Resondents were given a 7-item likert scale for which to answer from Strongly agree to Strongly disagree, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Strongly agree, whilst 0 represents an average response of Strongly disagree.

	How much do you agree or disagree with the following statement?
It is more environmentally sustainable to waste less food	0.777686
It is important to avoid wasting food to help alleviate climate change	0.652881
It saves me money to waste less food	0.854913

---

Energy (Community Solar vs Green Electricity scores)

To understand if the national index values for buying green energy also apply to community solar

What we want to be able to say

• What are people's perception of the efficacy of community solar, and whether it's their responsibility to adopt
• Are the values between green energy at large and community solar effectively the same?
• Where do they diverge?
• Is studying one a reasonable proxy for the other?

First we begin with having a look at participant responses for the Green Electricity questions

c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

Now onto the Community Solar questions

How do these compare?

Below you can see a comparison of normalized, weighted averages for each question that was asked on the topic of energy. Since we wanted to know whether answers in the Community Solar condition were equivalemnt to those in the Green Electricity condition, we set a bound of +/- 10% on the green electricity average and contrasted the community solar average to it. A significant difference therefore represents a difference in %responses greater than +/- 10%.

	Outcome efficacy from purchasing	Outcome efficacy from installing	Responsibility belongs supply-side	Consideration	Adoption	Belief that others have adopted	Belief that others should adopt	Belief that others think people should adopt	Cost relative usual	Intention	Perceived community benefit	Perceived personal benefit	Self-efficacy	Program interest
Community Solar	0.587223	0.651333	0.432768	0.239870	0.681163	0.169560	0.717196	0.424053	0.394500	0.245110	0.591282	0.549406	0.623166	0.484483
Green energy	0.578347	nan	0.464404	0.435934	0.711376	0.212070	0.763814	0.470735	0.444963	0.310991	0.599015	0.551799	0.572353	0.499770

The relevant difference threshold has been set at a  10.0% difference.

	Green energy	Community solar	diff	outcome	sig
variable
Adoption	0.567559	0.523424	0.044135	equivalent	0.000000
Perceived community benefit	0.599015	0.591282	0.007733	equivalent	0.000000
choice	0.450624	0.451708	-0.001084	equivalent	0.000000
Consideration	0.435934	0.239870	0.196064	non-equivalent	1.000000
Cost relative usual	0.444963	0.394500	0.050463	equivalent	0.000000
Belief that others have adopted	0.212070	0.169560	0.042510	equivalent	0.000000
Self-efficacy	0.572353	0.623166	-0.050813	equivalent	0.000001
Belief that others think people should adopt	0.470735	0.424053	0.046682	equivalent	0.000000
Intention	0.310991	0.245110	0.065882	equivalent	0.000193
Program interest	0.499770	0.484483	0.015287	equivalent	0.000000
Outcome efficacy from purchasing	0.578347	0.587223	-0.008876	equivalent	0.000000
Perceived personal benefit	0.551799	0.549406	0.002392	equivalent	0.000000
Belief that others should adopt	0.763814	0.717196	0.046617	equivalent	0.000041
Pay for own utility	0.944617	0.935618	0.008999	equivalent	0.000000
Responsibility belongs supply-side	0.464404	0.432768	0.031636	equivalent	0.000000

With a threshold of 10%, current results indicate that respondent's only non-equivalent set of answers is for the question of whether they had considered getting green/solar energy in the last 12 months.

Specifically:

• 'Before taking this survey, had you considered using green energy in the last 12 months?'
• 'Before taking this survey, had you considered using community solar in the last 12 months?'

'Before taking this survey, had you considered using community solar in the last 12 months?'

---

Electric Vehicles

To understand the public’s beliefs around EV rentals.

What we want to be able to say

• What are current thoughts and attitudes surrounding EV rentals
• Are the values between EV rental and purchasing effectively the same?
• Where do they diverge?

First we begin with having a look at participant responses for the EV Rental attitude questions. We also include "other" responses to a few questions that included longform options.

c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

	education	income	age_group	SASSY	politics	state	sex	In the last year, how many separate times have you rented a car? Car rentals can be daily or hourly rentals	In the last year, how many times have you rented a fully electric car? Car rentals can be daily or hourly rentals	Something else - Please specify::Considering the car rentals you made in the last year, what was the primary purpose of your car rental?	Something else - Please specify::What is most important to you when choosing a rental car?	Something else - Please specify::What do you think would be the most important to you if you were to choose a rental car?	Other - Please specify::Where do you think people who rent fully electric vehicles do the majority of their vehicle charging?	Other - Please specify::Where do you think drivers of fully electric cars do the majority of their vehicle charging?
Loading... (need help?)
None	education	income	age_group	SASSY	politics	state	sex	In the last year, how many separate times have you rented a car? Car rentals can be daily or hourly rentals	In the last year, how many times have you rented a fully electric car? Car rentals can be daily or hourly rentals	Something else - Please specify::Considering the car rentals you made in the last year, what was the primary purpose of your car rental?	Something else - Please specify::What is most important to you when choosing a rental car?	Something else - Please specify::What do you think would be the most important to you if you were to choose a rental car?	Other - Please specify::Where do you think people who rent fully electric vehicles do the majority of their vehicle charging?	Other - Please specify::Where do you think drivers of fully electric cars do the majority of their vehicle charging?

Now onto the EV purchasing attitude questions

c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

How do these compare?

Below you can see a comparison of normalized, weighted averages for each question that was asked on the topic of EVs. Since we wanted to know whether answers in the EV Rental condition were equivalemnt to those in the EV Purchasing condition, we set a bound of +/- 10% on the EV purchasing electricity average and contrasted the EV rental average to it. A significant difference therefore represents a difference in %responses greater than +/- 10%.

	Adoption	Consideration	Available in community	Belief that others have adopted	Belief that others should adopt	Belief that others think people should adopt	Cost relative usual	Perceived community benefit	Perceived personal benefit	Self-efficacy	Program interest	Rents or has car
EV Rentals	0.025409	0.574534	0.493390	0.202461	0.395169	0.253334	0.588914	0.442277	0.364458	0.401100	0.372836	0.262721
EV Purchases	0.043618	0.674310	nan	0.161729	0.412537	0.296019	0.528566	0.474137	0.441170	0.338985	0.442289	0.184227

The relevant difference threshold has been set at a  10.0% difference.

	EV Rentals	EV Purchases	diff	outcome	sig
variable
Adoption	0.025409	0.033167	-0.007758	equivalent	0.000000
Perceived community benefit	0.442277	0.474137	-0.031860	equivalent	0.000000
Consideration	0.179026	0.377418	-0.198392	non-equivalent	1.000000
Cost relative usual	0.588914	0.528566	0.060347	equivalent	0.000033
Belief that others have adopted	0.202461	0.161729	0.040732	equivalent	0.000000
Self-efficacy	0.401100	0.338985	0.062114	equivalent	0.000187
hasPotential	0.262721	0.909600	-0.646879	non-equivalent	1.000000
Belief that others think people should adopt	0.253334	0.296019	-0.042684	equivalent	0.000000
Program interest	0.372836	0.442289	-0.069453	equivalent	0.004102
Perceived personal benefit	0.364458	0.441170	-0.076711	equivalent	0.015739
Belief that others should adopt	0.395169	0.412537	-0.017369	equivalent	0.000000

With a threshold of 10%, current results indicate that respondent's only non-equivalent set of answers is for the question of whether they had considered getting green/solar energy in the last 12 months.

Specifically:

• 'Before taking this survey, had you considered using green energy in the last 12 months?'
• 'Before taking this survey, had you considered using community solar in the last 12 months?'

---

Food - Plant-Based Diet

To understand who people believe might be against talking about these actions.

What we want to be able to say

• Are people trying to eat less meat, and what are their motivations for it
• Are people talking about eating less meat, sharing it with those they know
• Where are the CC possibilities when it comes to meat-reduction interventions

A quick look at our meat-eating behaviors

This first set of questions examined whether respondents are trying to reduce their meat consumption, as well as the beliefs they hold about eating less meat.

The scores presented in the table represent the weighted average of respondents' likert responses. Resondents were given a 7-item likert scale for which to answer from Strongly agree to Strongly disagree, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Strongly agree, whilst 0 represents an average response of Strongly disagree.

What are some of the steps and reasons to reduce meat consumption given by the respondents?

	education	income	age_group	SASSY	politics	state	sex	Are you trying to limit your meat consumption?	Has successfully reduced meat eating in the last 5 years	Why are you trying to reduce/limit your meat consumption?	Can you tell us a bit more about the steps you are taking to reduce/limit your meat consumption?
Loading... (need help?)
None	education	income	age_group	SASSY	politics	state	sex	Are you trying to limit your meat consumption?	Has successfully reduced meat eating in the last 5 years	Why are you trying to reduce/limit your meat consumption?	Can you tell us a bit more about the steps you are taking to reduce/limit your meat consumption?

Next we look at respondents' sharing behavior. For those who are intending to eat less meat, are they talking about going meat-free with the people they know?

Respondents were asked wiith whom the had shared they were trying to eat less meat. The table represents the % respondents who intend to reduce their meat intake and who have shared with X member of their community.

They were also asked whether they thought their community would approve or dissaprove of their decision to reduce meat consumption.

	Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat?
Your family	76.392758
Your close friends	54.101835
Your colleagues	14.979179
Your neighbors	12.499653
Your social media followers	10.559358

c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

	education	income	age_group	SASSY	politics	state	sex	Are you trying to limit your meat consumption?	Has successfully reduced meat eating in the last 5 years	Other - Please specify::Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat?	Why have you shared, or why haven't you shared with others?	Please tell us who or which groups of people that would that be?	Who's guidance/tips would you prefer to receive about reducing your meat consumption?	Why would you prefer to receive advice from the group you selected?
Loading... (need help?)
None	education	income	age_group	SASSY	politics	state	sex	Are you trying to limit your meat consumption?	Has successfully reduced meat eating in the last 5 years	Other - Please specify::Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat?	Why have you shared, or why haven't you shared with others?	Please tell us who or which groups of people that would that be?	Who's guidance/tips would you prefer to receive about reducing your meat consumption?	Why would you prefer to receive advice from the group you selected?

Fianlly, we look at some of the potential avenues that ClimateCulture could explore when it comes to meat-reduction interventions

	How much do you agree or disagree with the following statement?
I am interested in learning how reducing my meat consumption can help my health and wellbeing	0.570174
I am interested in learning how reducing my meat consumption can benefit the environment	0.517047
I am interested in learning how reducing my meat consumption can benefit me financially	0.537400
I think positively about organizations that champion healthy food	0.673114
I think positively about organizations that champion environmentally sustainable food	0.628164

[NbConvertApp] Converting notebook Analysis_python.ipynb to html
[NbConvertApp] Writing 7484389 bytes to Analysis_python.html